Can Llama 3.2 11B Vision run on Radeon RX 7800M 12GB?

Q: What should I upgrade first if Llama 3.2 11B Vision feels slow on Radeon RX 7800M 12GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — Tight Fit

B65Good

Estimated from fit model

Llama 3.2 11B Vision needs ~11.1 GB VRAM. Radeon RX 7800M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.1 GB, 40.8 tok/s, Tight fit

11.1 GB required12.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

40.8 tok/s

TTFT

4741 ms

Safe context

16K

Memory

11.1 GB / 12.0 GB

Memory breakdown

Weights6.7 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsLlama 3.2 11B Vision on Radeon RX 7800M 12GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 40.8 tok/s decode · 4.7s TTFT (warm) · 102 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Tight fit	38.0 tok/s	2780 ms	16K
Coding	B	Tight fit	38.0 tok/s	5097 ms	16K
Agentic Coding	C	Very compromised	24.0 tok/s	11729 ms	16K
Reasoning	B	Tight fit	38.0 tok/s	6023 ms	16K
RAG	C	Very compromised	24.0 tok/s	14662 ms	16K

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on Radeon RX 7800M 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.3 GB	Low	B65
Q3_K_S	3	5.4 GB	Low	B67
NVFP4	4

Get started

Copy-paste commands to run Llama 3.2 11B Vision on your machine.

Run

ollama run llama3.2-vision:11b

Upgrade options

Hardware that runs Llama 3.2 11B Vision well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.26.8 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.32.3 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$349 MSRP

RX 9070 16GBAMD upgrade

16 GB VRAM (+4)640 GB/s (+208)

Raises estimated decode speed by about 56%.63.6 tok/s decode

Raises estimated decode speed by about 56%.

Adds memory headroom for longer context windows and future model growth.

~$479 MSRP

Frequently asked questions

Can Radeon RX 7800M 12GB run Llama 3.2 11B Vision?

Yes, Radeon RX 7800M 12GB can run Llama 3.2 11B Vision with a B grade (Tight fit). Expected decode speed: 38.0 tok/s.

How much VRAM does Llama 3.2 11B Vision need?

Llama 3.2 11B Vision (11B parameters) requires approximately 11.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.2 11B Vision?

The recommended quantization for Llama 3.2 11B Vision is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.2 11B Vision run at on Radeon RX 7800M 12GB?

On Radeon RX 7800M 12GB, Llama 3.2 11B Vision achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5097ms using Q4_K_M quantization.

Can Radeon RX 7800M 12GB run Llama 3.2 11B Vision for coding?

For coding workloads, Llama 3.2 11B Vision on Radeon RX 7800M 12GB receives a B grade with 38.0 tok/s and 16K context.

What context window can Llama 3.2 11B Vision use on Radeon RX 7800M 12GB?

On Radeon RX 7800M 12GB, Llama 3.2 11B Vision can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for Radeon RX 7800M 12GB See all hardware for Llama 3.2 11B Vision

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/llama-3.2-11b-vision-on-rx-7800m-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: